Year: 2015
Type: article
Abstract: BACKGROUND: The interpretation of the results from genome-scale experiments is a challenging and important problem in contemporary biomedical research. Biological networks that integrate experimental results with existing knowledge from biomedical databases and published literature can provide a rich resource and powerful basis for hypothesizing about mechanistic explanations for observed gene-phenotype relationships. However, the size and density of such networks often impede their efficient exploration and understanding. RESULTS: We introduce a visual analytics approach that integrates interactive filtering of dense networks based on degree-of-interest functions with attribute-based layouts of the resulting subnetworks. The comparison of multiple subnetworks representing different analysis facets is facilitated through an interactive super-network that integrates brushing-and-linking techniques for highlighting components across networks. An implementation is freely available as a Cytoscape app. CONCLUSIONS: We demonstrate the utility of our approach through two case studies using a dataset that combines clinical data with high-throughput data for studying the effect of β-blocker treatment on heart failure patients. Furthermore, we discuss our team-based iterative design and development process as well as the limitations and generalizability of our approach.
Source: BMC Bioinformatics
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,445
Awards: Knowledge-Based Biomedical Data Science, G1200543963, G1591429387, G166213354, G169650328, G2572216579, G3005369029, G3451935877, Genetic-Adrenergic Interactions in Myocardial Failure, Post Graduate Studies in Cardiovascular Research, G5848177503, Dynamic Expression Profiling in the Intact Human Heart, G799284092, G9000200170, G9000225109
OpenAlex ID: W1997294932